EFFICIENCY EVALUATION IN PUBLIC ROAD TRANSPORT: A STOCHASTIC FRONTIER ANALYSIS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper measures the technical efficiency of 54 public road transport operators and investigates the degree to which various factors influence efficiency levels in these firms. The study makes an attempt to provide an overview of the general status of different operators in 18 countries. Stochastic Frontier Analysis (SFA) methods are applied to our sample over a twelve year period from 2000 to 2011. To our knowledge, this is the first comprehensive analysis of technical efficiency of public road transport operators in 18 countries using parametric method. Our empirical results indicate that investment, operating profit and firm size have a significant influence on technical efficiency levels. We find that technical efficiency level of public road transport operators varies between 0.458 and 0.95. We also observe that large-size operators with more investment capacity tend to be more technically efficient than small-size operators. Finally, we find that operators from developed countries are technically more efficient than those of developing countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it